Compare Papers
Paper 1
A simple decoder for topological codes
James R. Wootton
- Year
- 2013
- Journal
- arXiv preprint
- DOI
- arXiv:1310.2393
- arXiv
- 1310.2393
Here we study an efficient algorithm for decoding the topological codes. It is based on a simple principle, which should allow straightforward generalization to complex decoding problems. It is benchmarked with the planar code for both i.i.d. and spatially correlated errors and is found to compare well with existing methods.
Open paperPaper 2
Computational Complexity of Learning Efficiently Generatable Pure States
Taiga Hiroka, Min-Hsiu Hsieh
- Year
- 2024
- Journal
- arXiv preprint
- DOI
- arXiv:2410.04373
- arXiv
- 2410.04373
Understanding the computational complexity of learning efficient classical programs in various learning models has been a fundamental and important question in classical computational learning theory. In this work, we study the computational complexity of quantum state learning, which can be seen as a quantum generalization of distributional learning introduced by Kearns et.al [STOC94]. Previous works by Chung and Lin [TQC21], and Bădescu and O$'$Donnell [STOC21] study the sample complexity of the quantum state learning and show that polynomial copies are sufficient if unknown quantum states are promised efficiently generatable. However, their algorithms are inefficient, and the computational complexity of this learning problem remains unresolved. In this work, we study the computational complexity of quantum state learning when the states are promised to be efficiently generatable. We show that if unknown quantum states are promised to be pure states and efficiently generateable, then there exists a quantum polynomial time algorithm $A$ and a language $L \in PP$ such that $A^L$ can learn its classical description. We also observe the connection between the hardness of learning quantum states and quantum cryptography. We show that the existence of one-way state generators with pure state outputs is equivalent to the average-case hardness of learning pure states. Additionally, we show that the existence of EFI implies the average-case hardness of learning mixed states.
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